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Gyroscope sensitivity is the gain that converts a true angular rate into the sensor’s reported rate. Bias is the rate a gyro reports when it is actually still, and bias stability describes how that apparent zero changes over time. Noise is the random, time-dependent variation in the measurement; drift is the accumulated effect of bias changes and other low-frequency errors after rate data is integrated into angle.
These specifications answer different questions. Sensitivity tells you whether the scale is too high or too low. Bias stability tells you how long a calibrated zero remains useful. Noise tells you how much the output wanders over a selected bandwidth and averaging time. A gyro with excellent short-term noise can still perform poorly in a vibrating vehicle if temperature, acceleration sensitivity, cross-axis coupling, or calibration dominate the error budget.
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What the specifications mean
Sensitivity (scale factor)
Sensitivity is the transfer-function gain between angular rate and output. If the true rate is 100 degrees per second and a sensor reports 101 degrees per second, the one-percent difference is a scale-factor error. The error grows in proportion to the measured rate, so it matters most near the upper part of the intended range and whenever measurements must be compared across axes or sensors.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThe ADIS16501 datasheet defines nominal sensitivity as “the slope of the line of best fit for the angular rate … transfer functions” over the full-scale range. In practical terms, calibration fits a line to known input rates; the slope is sensitivity and the intercept is the zero-rate bias. They must be calibrated separately. Changing the full-scale range, temperature, supply conditions, filtering, or digital configuration can change the effective gain.
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Bias and zero-rate offset
Bias is the output reported when the true angular rate is zero. A constant bias of 0.2 degrees per second appears small on a rate display, but integrating it for one minute produces 12 degrees of angle error if nothing estimates and removes it. Bias can vary with temperature, time, mechanical stress, supply conditions, and recent motion.
Bias stability
In-run bias stability describes the statistical wander of the apparent zero during a run. It is not a promise that the zero will remain inside a fixed band forever, nor is it a single permanent offset that can be removed once at the factory. Analog Devices’ MEMS IMU glossary notes that in-run bias stability is found as the minimum of the Allan-variance (AVAR) plot. The minimum occurs at the averaging time where further averaging no longer reduces white noise because low-frequency drift has begun to dominate.
Because the value is tied to an averaging time and a test setup, two datasheets can report different bias-stability numbers for the same hardware without either being wrong. Always ask how long the unit was stationary, what temperature and bandwidth were used, and whether the result is typical or guaranteed.
Noise
Gyroscope noise is random variation around the underlying rate. Its apparent size depends on bandwidth, sample rate, output filtering, and the time interval over which data is averaged. Short averaging intervals commonly expose white rate noise. Expressed as angle random walk (ARW), that short-term behavior describes how uncertainty in integrated angle grows with time when white noise is the main limitation.
Longer intervals can reveal flicker noise, bias instability, rate random walk, or a rate ramp. These are not interchangeable labels: each has a different slope on an Allan-deviation plot and a different implication for navigation or control.
Drift
Drift is an engineering description of the changing error that appears in an integrated attitude or heading estimate. It can come from a fixed bias, temperature-dependent bias, random walk, vibration rectification, or an uncorrected scale-factor and cross-axis error. Calling every visible wander “noise” hides whether averaging will help. White noise averages down; a slowly moving bias does not.
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| Term | What it describes | Typical question it answers |
|---|---|---|
| Sensitivity or scale factor | Gain from true angular rate to reported rate | Does the gyro read proportionally high or low? |
| Bias | Output at zero true rate | What does the gyro report while stationary right now? |
| Bias stability | Statistical change in apparent zero during a run | How long is a stationary calibration useful? |
| Noise density or ARW | Random short-term variation, dependent on bandwidth and averaging | How much does averaging reduce short-term error? |
| Drift | Accumulated angle or attitude error from bias and low-frequency processes | How quickly does an unaided orientation estimate depart from reality? |
Why the distinction matters in a real system
A scale-factor error is proportional to motion. If a controller commands a known rotation and the gyro’s gain is wrong, the error follows the commanded rate and can often be corrected with calibration at several rates. Bias is different: it creates an error even when the platform is motionless. A navigation filter may estimate bias as a state, but that estimate becomes weak when the system has no external reference or when motion is too brief.
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Noise affects control bandwidth and the confidence assigned to each measurement. Increasing the measurement bandwidth preserves faster motion but admits more white noise. Filtering reduces visible noise at the cost of latency and potentially attenuated dynamics. Averaging can improve a stationary estimate, yet it cannot remove a bias that is changing during the averaging interval.
In an integrated angle estimate, a constant rate error grows linearly with time. White rate noise produces a random-walk-like angle uncertainty whose growth depends on ARW and elapsed time. Low-frequency bias instability and rate random walk can eventually dominate, so a specification measured over seconds cannot be extrapolated indefinitely to hours.
How to read an Allan variance or Allan deviation plot
Allan variance is a time-domain method for analyzing a sequence of gyro data as a function of averaging time. NXP/Freescale’s 2015 application note, Allan Variance: Noise Analysis for Gyroscopes, describes it as “a method of analyzing a sequence of data in the time domain.” In most engineering reports, the square root of Allan variance—Allan deviation—is plotted because it has the same units as the measured rate.
1. Collect a stationary record
Mount the gyro securely and keep it motionless for a record long enough to cover the averaging times of interest. Record the raw rate, timestamps, configuration, temperature, and any external vibration. A short capture may show only white noise and cannot establish long-term stability. The record must be long enough that the largest cluster times on the plot contain meaningful independent segments.
2. Document the measurement conditions
- Sample rate and output data rate.
- Total recording duration.
- Temperature, warm-up state, and temperature variation.
- Mechanical mounting and vibration environment.
- Analog and digital bandwidth, filtering, and full-scale range.
- Calibration state, axis orientation, and output units.
3. Compute progressively longer cluster times
Partition the stationary sequence into clusters, calculate the average rate in each cluster, and evaluate the change between adjacent clusters for increasing averaging times. Plot Allan deviation against averaging time on logarithmic axes. The exact estimator and overlapping or non-overlapping implementation should be stated because they affect confidence and the number of usable points.
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4. Identify the short-time slope
The downward region at short averaging times means averaging is reducing white rate noise. Its slope is used to estimate angle random walk. The numerical conversion depends on the convention, units, and whether the plot uses Allan deviation or variance, so report the fitted region and formula rather than quoting a slope without context.
5. Find the minimum
The lowest point is the in-run bias-stability estimate, with the averaging time at that minimum. This is the practical compromise between white-noise reduction and emerging low-frequency drift. A value without its corresponding averaging time is incomplete.
6. Interpret the rising long-time regions
Upward slopes at longer averaging times indicate processes such as flicker noise, rate random walk, or rate ramp. NXP/Freescale and MathWorks use Allan-variance workflows to estimate parameters commonly called N, K, and B for angle random walk, rate random walk, and bias instability. A straight-looking segment is not proof that one physical mechanism is the only cause; temperature changes, vibration, clock errors, and insufficient record length can create similar features.
How to report a number responsibly
Attach the test conditions to every sensitivity, noise, or stability figure. State whether the value is a typical measurement, a limit, or a guaranteed maximum. Include the sample rate, record duration, temperature, vibration state, bandwidth and filtering, averaging-time range, calibration procedure, axis, and units. Distinguish degrees per second from radians per second, and degrees per square-root hour from degrees per square-root second.
For example, a bias-instability value of 0.9 degrees per hour is not directly comparable with a noise-density value in degrees per second per square-root hertz. They describe different phenomena and use different normalizations. Convert units only after identifying the underlying quantity and its averaging convention.
Representative IMX-6 figures and their limits
InertialSense documentation reports an example 0.10 degrees per square-root hour angle random walk and 0.9–0.95 degrees per hour bias instability for its IMX-6. The stated conditions are static, vibration-free operation at 25 °C, 1000 Hz, over 24 hours. These are vendor-specific example results, not universal limits for all gyroscopes or a guarantee that every unit will achieve them.
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The conditions explain why the figures cannot be transplanted directly into a vehicle specification. A vibrating installation, a different output filter, a changing temperature, or a shorter record can produce a different Allan-deviation curve. Use the figures as a reference for the reported test setup and request equivalent data when comparing another device.
A comparison framework for choosing a gyro
Compare sensors on like-for-like conditions instead of selecting the smallest single number. The following axes expose the trade-offs that matter in an application:
| Comparison axis | What to request | Why it matters |
|---|---|---|
| Sensitivity or scale-factor accuracy | Gain error over the intended full-scale rate and temperature range | Determines proportional rate error during motion |
| Short-term noise | Noise density or ARW with bandwidth and sample rate | Sets control and short-term attitude uncertainty |
| Long-term stability | In-run bias stability and the averaging time at the curve minimum | Indicates how quickly a stationary calibration loses value |
| Environmental sensitivity | Temperature coefficient, acceleration or vibration sensitivity, and cross-axis effects | Often dominates outside a quiet laboratory |
| Test method | Stationary duration, Allan processing, calibration state, and units | Determines whether published values are comparable |
A lower bias-stability number does not automatically make a gyro better in a vibrating vehicle. Vibration-induced errors, acceleration sensitivity, thermal gradients, mounting stress, and calibration quality can exceed the quiet-room value. Define the application error budget first, then select the specification that limits the dominant term.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Practical calibration and troubleshooting
When the rate is proportionally wrong
- Verify the selected full-scale range and output units.
- Check scale-factor calibration at more than one known rate if possible.
- Repeat the test at the operating temperature range; gain can be temperature dependent.
- Inspect axis mapping and cross-axis alignment before changing software gain.
When a stationary output is offset
- Allow the sensor to reach thermal equilibrium and record temperature with the rate.
- Perform a zero-rate calibration in the actual mounting orientation.
- Check whether the platform is truly stationary; tiny vibration can appear as a bias change.
- Use an external reference or filter to track bias when the application permits it.
When the Allan plot has an unexpected rise
- Compare the rise with the temperature record to find thermal correlation.
- Repeat with improved isolation to separate vibration from intrinsic noise.
- Confirm that the record is long enough and that timestamp gaps are absent.
- Check filtering and sample-rate changes, which can alter the short-time slope.
Do not “fix” a changing bias by applying a single offset measured at startup. That removes only the initial intercept. A temperature model, bias-state estimator, periodic stationary updates, or an external attitude reference is needed when the zero continues to move.
Design decisions: averaging, filtering, and external references
Choose the averaging time from the mission requirement. If the platform must detect a rapid maneuver, averaging beyond the maneuver duration blurs the event and adds latency. If it only needs a stable estimate while stationary, averaging near the Allan minimum can substantially reduce white noise before bias drift takes over.
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Frequently asked questions
Is bias stability the same as repeatability?
No. Bias stability is a stochastic in-run measure derived from a time series. Repeatability usually asks whether separate starts, power cycles, temperatures, or units return to the same bias. A sensor can have a good in-run minimum but poor restart repeatability.
Can I compare two Allan plots with different record lengths?
Only cautiously. The shorter record cannot support the same long averaging times, so its apparent minimum may occur before the other plot’s minimum simply because later behavior was not observed.
Does more sample rate always mean less noise?
No. A higher output rate can provide more bandwidth or more samples for processing, but the underlying sensor noise, digital filtering, quantization, and bandwidth determine the result. Compare noise density or ARW with the stated filter, not sample rate alone.
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Why can a gyro look quiet on a bench but drift in a vehicle?
The bench may be vibration-free and thermally stable, while a vehicle adds vibration, acceleration, mounting stress, temperature gradients, and cross-axis motion. Those environmental sensitivities are separate from the stationary Allan result.
What single number should I use in an error budget?
There is no universal substitute. Use scale-factor error for proportional motion, ARW or noise density for short-term uncertainty, bias stability for the relevant hold time, and measured environmental coefficients for temperature and vibration. Preserve each number’s units and test conditions.
Frequently Asked Questions
Is bias stability the same as repeatability?
No. Bias stability is a stochastic in-run measure from a time series; repeatability compares bias across starts, power cycles, temperatures, or units.
Can I compare Allan plots made with different record lengths?
Only cautiously. A shorter record cannot support the same long averaging times, so it may miss the later minimum or drift region.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Does a higher sample rate guarantee lower gyro noise?
No. Noise density, bandwidth, filtering, quantization, and the sensor itself determine noise; sample rate alone is not a quality metric.
Why does a gyro drift more in a vehicle than on a bench?
Vehicles add vibration, acceleration, mounting stress, temperature gradients, and cross-axis motion that a quiet stationary test may not include.
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